Entropy regularization makes planning problems mathematically smoother, enabling algorithms with provable efficiency guarantees that don't exist for standard reinforcement learning.
SmoothCruiser is a planning algorithm that efficiently estimates value functions in entropy-regularized decision-making problems. By leveraging the smoothness that entropy regularization provides, it achieves polynomial sample complexity guarantees—a significant improvement over non-regularized approaches where no such guarantees exist.